Robust Coordinated Reinforcement Learning for MAC Design in Sensor Networks

In this paper, we propose a medium access control (MAC) design method for wireless sensor networks based on decentralized coordinated reinforcement learning. Our solution maps the MAC resource allocation problem first to a factor graph, and then, based on the dependencies between sensors, transforms...

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Vydané v:IEEE journal on selected areas in communications Ročník 37; číslo 10; s. 2211 - 2224
Hlavní autori: Nisioti, Eleni, Thomos, Nikolaos
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.10.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Access control
Algorithms
Complexity
Complexity theory
Computer simulation
Convergence
Coordination
coordination graphs
irregular repetition slotted ALOHA
Knapsack problem
Machine learning
max-sum algorithm
Media Access Protocol
Medium access control
Optimization
POMDP
Q-learning
Reinforcement learning
Remote sensors
Resource allocation
Resource management
Sensors
Throughput
Wireless sensor networks
ISSN:0733-8716, 1558-0008
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Shrnutí:In this paper, we propose a medium access control (MAC) design method for wireless sensor networks based on decentralized coordinated reinforcement learning. Our solution maps the MAC resource allocation problem first to a factor graph, and then, based on the dependencies between sensors, transforms it into a coordination graph, on which the max-sum algorithm is employed to find the optimal transmission actions for sensors. We have theoretically analyzed the system and determined the convergence guarantees for decentralized coordinated learning in sensor networks. As part of this analysis, we derive a novel sufficient condition for the convergence of max-sum on graphs with cycles and employ it to render the learning process robust. In addition, we reduce the complexity of applying max-sum to our optimization problem by expressing coordination as a multiple knapsack problem (MKP). The complexity of the proposed solution can be, thus, bounded by the capacities of the MKP. Our simulations reveal the benefits coming from adaptivity and sensors' coordination, both inherent in the proposed learning-based MAC.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:0733-8716
1558-0008
DOI:10.1109/JSAC.2019.2933887